Multimodal image registration using histogram of oriented gradient distance and data-driven grey wolf optimizer

Abstract Multimodal image registration is becoming increasingly important in remote sensing. However, due to the significant nonlinear intensity differences between multimodal images, conventional registration methods tend to get trapped into local optima. To address this issue, we present a new approach for multimodal image registration using histogram of oriented gradient distance (HOGD) and data-driven grey wolf optimizer (DDGWO). First, we propose a novel similarity measure for area-based registration methods that is HOGD. We investigate the performance of HOGD by analyzing its similarity curve. HOGD has a large range of values, which is helpful to find the global optimum. Second, we use GWO to optimize the transformation parameters. Since it is time-consuming to calculate HOGD, we propose DDGWO to minimize HOGD. In DDGWO, the iterations are divided into two parts: the training and prediction iterations. A support vector machine (SVM) regression model is trained by the historical HOGD computed in the training iterations. The trained SVM model predicts HOGD instead of calculating in the prediction iterations, which can reduce the computational time. Finally, we test the proposed approach that uses HOGD as the similarity measure and DDGWO as the search algorithm on 12 real and four simulated image pairs. Extensive experiments demonstrate that our approach saves up to 83.35-84.15% of computational time and outperforms the state-of-the-art algorithms in terms of registration accuracy.